Stochastic models of epidemic super-spreading events

10 Jul 2018, 11:00
New Law School/--107 (University of Sydney)

New Law School/--107

University of Sydney

Oral Presentation Minisymposium: Women Advancing Mathematical Biology Women advancing mathematical biology


Angela Peace (Texas Tech University)


The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, that are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus non-superspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.

Primary authors

Angela Peace (Texas Tech University) Dr Linda Allen (Texas Tech University ) Dr Christina Edholm (University of Tennessee ) Dr Blessing Emerenini (Ryerson University ) Dr Anarina Murillo (University of Alabama at Birmingham ) Dr Omar Saucedo (The Ohio State University ) Dr Nika Shakiba (University of Toronto ) Dr Xueying Wang (Washington State University )

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